Overview

Dataset statistics

Number of variables14
Number of observations52704
Missing cells7694
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 MiB
Average record size in memory112.0 B

Variable types

DateTime1
Numeric13

Alerts

Power (kW) is highly overall correlated with Rear bearing temperature (°C) and 6 other fieldsHigh correlation
Wind direction (°) is highly overall correlated with Nacelle position (°)High correlation
Nacelle position (°) is highly overall correlated with Wind direction (°)High correlation
Rear bearing temperature (°C) is highly overall correlated with Power (kW) and 4 other fieldsHigh correlation
Rotor speed (RPM) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Generator RPM (RPM) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Front bearing temperature (°C) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Tower Acceleration X (mm/ss) is highly overall correlated with Power (kW) and 4 other fieldsHigh correlation
Wind speed (m/s) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Tower Acceleration y (mm/ss) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
blade_angle has 709 (1.3%) missing valuesMissing
Rear bearing temperature (°C) has 709 (1.3%) missing valuesMissing
Nacelle ambient temperature (°C) has 709 (1.3%) missing valuesMissing
Front bearing temperature (°C) has 709 (1.3%) missing valuesMissing
Tower Acceleration X (mm/ss) has 709 (1.3%) missing valuesMissing
Tower Acceleration y (mm/ss) has 709 (1.3%) missing valuesMissing
Metal particle count counter has 709 (1.3%) missing valuesMissing
# Date and time has unique valuesUnique
blade_angle has 22677 (43.0%) zerosZeros
Rotor speed (RPM) has 1051 (2.0%) zerosZeros

Reproduction

Analysis started2023-07-08 11:59:50.860651
Analysis finished2023-07-08 12:00:07.826461
Duration16.97 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct52704
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size411.9 KiB
Minimum2020-01-01 00:00:00
Maximum2020-12-31 23:50:00
2023-07-08T17:30:07.990298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:08.082790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Power (kW)
Real number (ℝ)

Distinct52216
Distinct (%)99.9%
Missing456
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean688.41732
Minimum-16.781887
Maximum2081.81
Zeros3
Zeros (%)< 0.1%
Negative5148
Negative (%)9.8%
Memory size411.9 KiB
2023-07-08T17:30:08.183144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-16.781887
5-th percentile-1.8511468
Q1130.12633
median436.78474
Q31137.6185
95-th percentile2024.9276
Maximum2081.81
Range2098.5919
Interquartile range (IQR)1007.4922

Descriptive statistics

Standard deviation672.32571
Coefficient of variation (CV)0.97662522
Kurtosis-0.67780413
Mean688.41732
Median Absolute Deviation (MAD)380.31487
Skewness0.83153265
Sum35968428
Variance452021.86
MonotonicityNot monotonic
2023-07-08T17:30:08.277308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
< 0.1%
-1.210202656 2
 
< 0.1%
-1.223849028 2
 
< 0.1%
-1.976617518 2
 
< 0.1%
-0.7244930267 2
 
< 0.1%
2053.320367 2
 
< 0.1%
2027.35542 2
 
< 0.1%
2032.112939 2
 
< 0.1%
-3 2
 
< 0.1%
-0.716490519 2
 
< 0.1%
Other values (52206) 52227
99.1%
(Missing) 456
 
0.9%
ValueCountFrequency (%)
-16.78188663 1
< 0.1%
-15.91170658 1
< 0.1%
-15.24150906 1
< 0.1%
-14.73568258 1
< 0.1%
-14.65643455 1
< 0.1%
-14.59493494 1
< 0.1%
-13.79385256 1
< 0.1%
-13.77146039 1
< 0.1%
-13.69587858 1
< 0.1%
-13.44251127 1
< 0.1%
ValueCountFrequency (%)
2081.809985 1
< 0.1%
2080.674628 1
< 0.1%
2076.961378 1
< 0.1%
2076.873773 1
< 0.1%
2076.131104 1
< 0.1%
2075.646625 1
< 0.1%
2074.546997 1
< 0.1%
2074.436548 1
< 0.1%
2073.681073 1
< 0.1%
2072.970416 1
< 0.1%

Wind direction (°)
Real number (ℝ)

Distinct52248
Distinct (%)> 99.9%
Missing455
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean195.50637
Minimum0.0096075552
Maximum359.98944
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:30:08.370404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0096075552
5-th percentile27.232004
Q1142.76444
median213.72975
Q3254.51901
95-th percentile325.70465
Maximum359.98944
Range359.97983
Interquartile range (IQR)111.75457

Descriptive statistics

Standard deviation91.200954
Coefficient of variation (CV)0.46648584
Kurtosis-0.6162501
Mean195.50637
Median Absolute Deviation (MAD)49.506786
Skewness-0.5444013
Sum10215012
Variance8317.6141
MonotonicityNot monotonic
2023-07-08T17:30:08.466148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85.83000183 2
 
< 0.1%
110.0028248 1
 
< 0.1%
178.8083562 1
 
< 0.1%
179.0687385 1
 
< 0.1%
184.1155971 1
 
< 0.1%
179.9187483 1
 
< 0.1%
174.8400049 1
 
< 0.1%
169.6985783 1
 
< 0.1%
167.5817531 1
 
< 0.1%
172.9070778 1
 
< 0.1%
Other values (52238) 52238
99.1%
(Missing) 455
 
0.9%
ValueCountFrequency (%)
0.009607555176 1
< 0.1%
0.02282381189 1
< 0.1%
0.03007944239 1
< 0.1%
0.03880837922 1
< 0.1%
0.05180043596 1
< 0.1%
0.06317940616 1
< 0.1%
0.104770012 1
< 0.1%
0.1564096155 1
< 0.1%
0.1711356662 1
< 0.1%
0.1746983711 1
< 0.1%
ValueCountFrequency (%)
359.9894394 1
< 0.1%
359.9757755 1
< 0.1%
359.9501575 1
< 0.1%
359.9435577 1
< 0.1%
359.9281253 1
< 0.1%
359.9161632 1
< 0.1%
359.9138971 1
< 0.1%
359.9057739 1
< 0.1%
359.8767395 1
< 0.1%
359.8676119 1
< 0.1%

Nacelle position (°)
Real number (ℝ)

Distinct12672
Distinct (%)24.3%
Missing455
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean195.5995
Minimum0.07583598
Maximum359.9303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:30:08.567683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.07583598
5-th percentile26.07452
Q1143.51227
median214.85452
Q3255.46497
95-th percentile326.16183
Maximum359.9303
Range359.85447
Interquartile range (IQR)111.9527

Descriptive statistics

Standard deviation91.519505
Coefficient of variation (CV)0.46789233
Kurtosis-0.62411838
Mean195.5995
Median Absolute Deviation (MAD)50.106401
Skewness-0.54718488
Sum10219878
Variance8375.8198
MonotonicityNot monotonic
2023-07-08T17:30:08.663567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199.4891663 254
 
0.5%
197.2929382 241
 
0.5%
251.0741577 207
 
0.4%
203.879425 205
 
0.4%
254.3674011 201
 
0.4%
255.4649658 187
 
0.4%
222.5374756 185
 
0.4%
258.7576599 184
 
0.3%
191.8051147 179
 
0.3%
34.85500717 176
 
0.3%
Other values (12662) 50230
95.3%
(Missing) 455
 
0.9%
ValueCountFrequency (%)
0.07583597951 1
< 0.1%
0.1431178238 1
< 0.1%
0.1543363559 1
< 0.1%
0.3068654073 1
< 0.1%
0.309737721 1
< 0.1%
0.4019045331 1
< 0.1%
0.437467294 1
< 0.1%
0.5117836299 1
< 0.1%
0.5549860121 1
< 0.1%
0.6015495117 1
< 0.1%
ValueCountFrequency (%)
359.9303019 1
 
< 0.1%
359.8478898 1
 
< 0.1%
359.7336121 16
 
< 0.1%
359.7330627 51
0.1%
359.7325134 11
 
< 0.1%
359.6948192 1
 
< 0.1%
359.6720405 1
 
< 0.1%
359.6073301 1
 
< 0.1%
359.5924296 1
 
< 0.1%
359.5818724 1
 
< 0.1%

blade_angle
Real number (ℝ)

MISSING  ZEROS 

Distinct21193
Distinct (%)40.8%
Missing709
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean5.7709342
Minimum0
Maximum92.496663
Zeros22677
Zeros (%)43.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:30:08.767140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.057333334
Q31.49
95-th percentile44.990002
Maximum92.496663
Range92.496663
Interquartile range (IQR)1.49

Descriptive statistics

Standard deviation15.15527
Coefficient of variation (CV)2.6261381
Kurtosis12.239958
Mean5.7709342
Median Absolute Deviation (MAD)0.057333334
Skewness3.3815202
Sum300059.72
Variance229.68222
MonotonicityNot monotonic
2023-07-08T17:30:08.859914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22677
43.0%
44.99000168 2716
 
5.2%
0.02450000048 459
 
0.9%
89.98999786 395
 
0.7%
44.99333445 371
 
0.7%
1.49000001 236
 
0.4%
0.04900000095 181
 
0.3%
0.02449974513 179
 
0.3%
1.49000001 90
 
0.2%
0.0489995709 83
 
0.2%
Other values (21183) 24608
46.7%
(Missing) 709
 
1.3%
ValueCountFrequency (%)
0 22677
43.0%
0.0001587301545 1
 
< 0.1%
0.0001587301552 1
 
< 0.1%
0.0001666666622 11
 
< 0.1%
0.0001666666629 18
 
< 0.1%
0.0001754385926 3
 
< 0.1%
0.0002564102507 1
 
< 0.1%
0.0003253968167 1
 
< 0.1%
0.0003333333244 4
 
< 0.1%
0.0003333333259 17
 
< 0.1%
ValueCountFrequency (%)
92.49666341 1
 
< 0.1%
92.49000041 2
 
< 0.1%
92.48999786 30
0.1%
92.48666636 3
 
< 0.1%
92.48333486 1
 
< 0.1%
92.48333486 1
 
< 0.1%
92.36719138 1
 
< 0.1%
92.32333374 13
< 0.1%
92.17099826 1
 
< 0.1%
92.12066358 1
 
< 0.1%

Rear bearing temperature (°C)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40983
Distinct (%)78.8%
Missing709
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean62.945578
Minimum8.83
Maximum75.0925
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:30:08.952064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8.83
5-th percentile41.975
Q161.23
median66.217501
Q368.485001
95-th percentile70.7775
Maximum75.0925
Range66.2625
Interquartile range (IQR)7.2550012

Descriptive statistics

Standard deviation9.5031954
Coefficient of variation (CV)0.15097479
Kurtosis7.1103951
Mean62.945578
Median Absolute Deviation (MAD)2.8249989
Skewness-2.449625
Sum3272855.4
Variance90.310723
MonotonicityNot monotonic
2023-07-08T17:30:09.044522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67.46749992 9
 
< 0.1%
67.8875 9
 
< 0.1%
68.275 8
 
< 0.1%
24.20000076 8
 
< 0.1%
68.39749985 8
 
< 0.1%
68.05249939 7
 
< 0.1%
69.04749985 7
 
< 0.1%
67.3125 7
 
< 0.1%
67.25 7
 
< 0.1%
68.42249947 7
 
< 0.1%
Other values (40973) 51918
98.5%
(Missing) 709
 
1.3%
ValueCountFrequency (%)
8.830000019 1
< 0.1%
8.934999752 1
< 0.1%
8.987499857 1
< 0.1%
9.025 1
< 0.1%
9.069999933 1
< 0.1%
9.215789343 1
< 0.1%
9.267500067 1
< 0.1%
9.340000153 1
< 0.1%
9.350000095 1
< 0.1%
9.400000095 1
< 0.1%
ValueCountFrequency (%)
75.09249992 1
< 0.1%
74.45999985 1
< 0.1%
74.10263302 1
< 0.1%
73.72749939 1
< 0.1%
73.5947362 1
< 0.1%
73.35263343 1
< 0.1%
73.29999971 1
< 0.1%
73.24999886 1
< 0.1%
73.23999939 1
< 0.1%
73.22249908 1
< 0.1%

Rotor speed (RPM)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51044
Distinct (%)97.7%
Missing455
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean10.665581
Minimum0
Maximum15.327491
Zeros1051
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:30:09.144812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.60150161
Q18.3323416
median11.062343
Q314.48181
95-th percentile15.167062
Maximum15.327491
Range15.327491
Interquartile range (IQR)6.1494679

Descriptive statistics

Standard deviation4.1284029
Coefficient of variation (CV)0.38707716
Kurtosis0.63092705
Mean10.665581
Median Absolute Deviation (MAD)2.8851879
Skewness-1.0264041
Sum557265.95
Variance17.04371
MonotonicityNot monotonic
2023-07-08T17:30:09.240158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1051
 
2.0%
8.140000343 42
 
0.1%
0.0110000018 9
 
< 0.1%
8.180000305 6
 
< 0.1%
8.260000229 6
 
< 0.1%
15.15999985 5
 
< 0.1%
0.01250000205 5
 
< 0.1%
0.01050000242 5
 
< 0.1%
0.01200000197 5
 
< 0.1%
8.239999771 4
 
< 0.1%
Other values (51034) 51111
97.0%
(Missing) 455
 
0.9%
ValueCountFrequency (%)
0 1051
2.0%
0.002068000264 1
 
< 0.1%
0.005386501201 1
 
< 0.1%
0.01050000242 5
 
< 0.1%
0.0110000018 9
 
< 0.1%
0.01103300181 1
 
< 0.1%
0.01150000188 3
 
< 0.1%
0.01166666936 1
 
< 0.1%
0.01200000197 5
 
< 0.1%
0.01210526514 1
 
< 0.1%
ValueCountFrequency (%)
15.32749082 1
< 0.1%
15.30796507 1
< 0.1%
15.30779167 1
< 0.1%
15.30725764 1
< 0.1%
15.30430314 1
< 0.1%
15.30217083 1
< 0.1%
15.28814529 1
< 0.1%
15.28658308 1
< 0.1%
15.28498332 1
< 0.1%
15.28475126 1
< 0.1%

Generator RPM (RPM)
Real number (ℝ)

Distinct52226
Distinct (%)> 99.9%
Missing455
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean1264.889
Minimum-98.162839
Maximum1815.5733
Zeros2
Zeros (%)< 0.1%
Negative5
Negative (%)< 0.1%
Memory size411.9 KiB
2023-07-08T17:30:09.343946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-98.162839
5-th percentile71.450867
Q1989.58152
median1312.8058
Q31715.8941
95-th percentile1797.3772
Maximum1815.5733
Range1913.7361
Interquartile range (IQR)726.31253

Descriptive statistics

Standard deviation488.70779
Coefficient of variation (CV)0.38636418
Kurtosis0.64217421
Mean1264.889
Median Absolute Deviation (MAD)341.49899
Skewness-1.0312781
Sum66089184
Variance238835.3
MonotonicityNot monotonic
2023-07-08T17:30:09.438657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
969.9099731 3
 
< 0.1%
969.9199829 3
 
< 0.1%
970.0200195 2
 
< 0.1%
968.3221436 2
 
< 0.1%
969.960022 2
 
< 0.1%
967.2241745 2
 
< 0.1%
970.0700073 2
 
< 0.1%
970.0100098 2
 
< 0.1%
1795.095709 2
 
< 0.1%
965.6444149 2
 
< 0.1%
Other values (52216) 52227
99.1%
(Missing) 455
 
0.9%
ValueCountFrequency (%)
-98.16283946 1
< 0.1%
-67.11200822 1
< 0.1%
-53.1367955 1
< 0.1%
-52.25690545 1
< 0.1%
-44.78196927 1
< 0.1%
0 2
< 0.1%
0.03999999911 1
< 0.1%
0.177782895 1
< 0.1%
0.2790928027 1
< 0.1%
0.4612312029 1
< 0.1%
ValueCountFrequency (%)
1815.573286 1
< 0.1%
1814.822591 1
< 0.1%
1814.378065 1
< 0.1%
1813.483555 1
< 0.1%
1813.047442 1
< 0.1%
1812.096582 1
< 0.1%
1811.286541 1
< 0.1%
1811.244404 1
< 0.1%
1811.210123 1
< 0.1%
1811.041803 1
< 0.1%
Distinct41679
Distinct (%)80.2%
Missing709
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean10.976653
Minimum-2.0799999
Maximum33.813889
Zeros0
Zeros (%)0.0%
Negative289
Negative (%)0.5%
Memory size411.9 KiB
2023-07-08T17:30:09.540234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2.0799999
5-th percentile3.13
Q16.7099998
median10.305263
Q314.720526
95-th percentile20.995711
Maximum33.813889
Range35.893889
Interquartile range (IQR)8.0105264

Descriptive statistics

Standard deviation5.5494491
Coefficient of variation (CV)0.50556843
Kurtosis0.0723221
Mean10.976653
Median Absolute Deviation (MAD)3.9547367
Skewness0.53884897
Sum570731.06
Variance30.796385
MonotonicityNot monotonic
2023-07-08T17:30:09.636295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.5 23
 
< 0.1%
6 21
 
< 0.1%
9 18
 
< 0.1%
6.400000095 17
 
< 0.1%
9.5 16
 
< 0.1%
7.400000095 16
 
< 0.1%
8.494999981 15
 
< 0.1%
8.49749999 15
 
< 0.1%
9.507500029 14
 
< 0.1%
12.10000038 14
 
< 0.1%
Other values (41669) 51826
98.3%
(Missing) 709
 
1.3%
ValueCountFrequency (%)
-2.079999924 1
 
< 0.1%
-2.032499969 1
 
< 0.1%
-2 3
< 0.1%
-1.994999999 1
 
< 0.1%
-1.992500007 1
 
< 0.1%
-1.990000004 1
 
< 0.1%
-1.962499994 1
 
< 0.1%
-1.947500014 1
 
< 0.1%
-1.935000002 1
 
< 0.1%
-1.90999999 1
 
< 0.1%
ValueCountFrequency (%)
33.81388919 1
< 0.1%
33.61578911 1
< 0.1%
33.55882263 1
< 0.1%
33.44999943 1
< 0.1%
33.34999982 1
< 0.1%
33.27894793 1
< 0.1%
33.27368405 1
< 0.1%
33.06499958 1
< 0.1%
33.03999977 1
< 0.1%
33.01749954 1
< 0.1%

Front bearing temperature (°C)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40389
Distinct (%)77.7%
Missing709
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean65.42145
Minimum9.6999998
Maximum80.525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:30:09.856017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9.6999998
5-th percentile42.129684
Q161.6775
median70.625
Q372.442501
95-th percentile73.995
Maximum80.525
Range70.825001
Interquartile range (IQR)10.765001

Descriptive statistics

Standard deviation10.894891
Coefficient of variation (CV)0.16653392
Kurtosis4.1128551
Mean65.42145
Median Absolute Deviation (MAD)2.7749996
Skewness-1.9516923
Sum3401588.3
Variance118.69865
MonotonicityNot monotonic
2023-07-08T17:30:09.949953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.60000038 17
 
< 0.1%
10.80000019 9
 
< 0.1%
71.23999939 9
 
< 0.1%
71.85 9
 
< 0.1%
25.39999962 9
 
< 0.1%
72.28500061 9
 
< 0.1%
71.25250015 9
 
< 0.1%
71.46250076 8
 
< 0.1%
71.67750015 8
 
< 0.1%
72.25750084 8
 
< 0.1%
Other values (40379) 51900
98.5%
(Missing) 709
 
1.3%
ValueCountFrequency (%)
9.699999809 2
< 0.1%
9.774999666 2
< 0.1%
9.899999619 2
< 0.1%
9.932499743 1
< 0.1%
9.957499838 1
< 0.1%
10 1
< 0.1%
10.03499999 1
< 0.1%
10.05999994 1
< 0.1%
10.1624999 1
< 0.1%
10.18999982 1
< 0.1%
ValueCountFrequency (%)
80.52500038 1
< 0.1%
80.10750046 1
< 0.1%
80.06499863 1
< 0.1%
79.89499931 1
< 0.1%
79.50000038 1
< 0.1%
79.46249962 1
< 0.1%
79.41249886 1
< 0.1%
79.39249954 1
< 0.1%
79.16388702 1
< 0.1%
79.14249954 1
< 0.1%

Tower Acceleration X (mm/ss)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct51993
Distinct (%)> 99.9%
Missing709
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean57.800081
Minimum2.4001763
Maximum235.24171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:30:10.049734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.4001763
5-th percentile4.5057825
Q136.77734
median55.919861
Q378.611788
95-th percentile109.90502
Maximum235.24171
Range232.84154
Interquartile range (IQR)41.834449

Descriptive statistics

Standard deviation31.001507
Coefficient of variation (CV)0.5363575
Kurtosis0.22831394
Mean57.800081
Median Absolute Deviation (MAD)20.765582
Skewness0.36799024
Sum3005315.2
Variance961.09344
MonotonicityNot monotonic
2023-07-08T17:30:10.146039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.16764116 2
 
< 0.1%
37.46486974 2
 
< 0.1%
102.5019813 1
 
< 0.1%
3.504272342 1
 
< 0.1%
4.007670647 1
 
< 0.1%
4.00061647 1
 
< 0.1%
4.372581883 1
 
< 0.1%
4.571584827 1
 
< 0.1%
3.911840338 1
 
< 0.1%
4.558708951 1
 
< 0.1%
Other values (51983) 51983
98.6%
(Missing) 709
 
1.3%
ValueCountFrequency (%)
2.400176287 1
< 0.1%
2.626677588 1
< 0.1%
2.768888963 1
< 0.1%
2.823034728 1
< 0.1%
2.849374089 1
< 0.1%
2.859521887 1
< 0.1%
2.86600537 1
< 0.1%
2.866847402 1
< 0.1%
2.872601193 1
< 0.1%
2.900696325 1
< 0.1%
ValueCountFrequency (%)
235.2417139 1
< 0.1%
221.9020229 1
< 0.1%
221.901796 1
< 0.1%
220.2855019 1
< 0.1%
220.2473353 1
< 0.1%
216.6273228 1
< 0.1%
214.9409359 1
< 0.1%
212.2709763 1
< 0.1%
212.0215 1
< 0.1%
211.500722 1
< 0.1%

Wind speed (m/s)
Real number (ℝ)

Distinct52153
Distinct (%)99.8%
Missing455
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean6.3865537
Minimum0.15937537
Maximum22.517229
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:30:10.245389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.15937537
5-th percentile2.2629877
Q14.1980462
median5.9829977
Q38.137287
95-th percentile11.882862
Maximum22.517229
Range22.357854
Interquartile range (IQR)3.9392408

Descriptive statistics

Standard deviation3.0078377
Coefficient of variation (CV)0.47096413
Kurtosis0.80008838
Mean6.3865537
Median Absolute Deviation (MAD)1.9308156
Skewness0.79021486
Sum333691.04
Variance9.0470875
MonotonicityNot monotonic
2023-07-08T17:30:10.339061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.849999905 3
 
< 0.1%
4.849999905 3
 
< 0.1%
2.299999952 3
 
< 0.1%
3.430000067 3
 
< 0.1%
2.960000038 3
 
< 0.1%
2.75999999 3
 
< 0.1%
6.7406075 2
 
< 0.1%
6.32042675 2
 
< 0.1%
2.480000019 2
 
< 0.1%
3.410000086 2
 
< 0.1%
Other values (52143) 52223
99.1%
(Missing) 455
 
0.9%
ValueCountFrequency (%)
0.1593753744 1
< 0.1%
0.2264804526 1
< 0.1%
0.2575689718 1
< 0.1%
0.258656479 1
< 0.1%
0.2714606861 1
< 0.1%
0.3027376547 1
< 0.1%
0.3051190127 1
< 0.1%
0.315000082 1
< 0.1%
0.3444562692 1
< 0.1%
0.3547875538 1
< 0.1%
ValueCountFrequency (%)
22.51722908 1
< 0.1%
22.04715014 1
< 0.1%
21.87478094 1
< 0.1%
21.70171847 1
< 0.1%
21.55368772 1
< 0.1%
21.53887386 1
< 0.1%
21.02927504 1
< 0.1%
20.98677669 1
< 0.1%
20.97071266 1
< 0.1%
20.88843741 1
< 0.1%

Tower Acceleration y (mm/ss)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct51995
Distinct (%)100.0%
Missing709
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean29.179751
Minimum2.5637798
Maximum222.05395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:30:10.437999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.5637798
5-th percentile4.7234756
Q117.593847
median26.418656
Q337.311932
95-th percentile60.466244
Maximum222.05395
Range219.49017
Interquartile range (IQR)19.718085

Descriptive statistics

Standard deviation17.822036
Coefficient of variation (CV)0.61076726
Kurtosis7.7576613
Mean29.179751
Median Absolute Deviation (MAD)9.6565249
Skewness1.7972192
Sum1517201.1
Variance317.62498
MonotonicityNot monotonic
2023-07-08T17:30:10.532584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.44070387 1
 
< 0.1%
4.101867619 1
 
< 0.1%
4.433738602 1
 
< 0.1%
4.761739102 1
 
< 0.1%
4.194383437 1
 
< 0.1%
4.458586693 1
 
< 0.1%
4.655320732 1
 
< 0.1%
3.896937129 1
 
< 0.1%
4.739453804 1
 
< 0.1%
3.983895473 1
 
< 0.1%
Other values (51985) 51985
98.6%
(Missing) 709
 
1.3%
ValueCountFrequency (%)
2.563779783 1
< 0.1%
2.685899019 1
< 0.1%
2.909165323 1
< 0.1%
2.963258296 1
< 0.1%
2.970664315 1
< 0.1%
2.994341326 1
< 0.1%
3.08077465 1
< 0.1%
3.082708221 1
< 0.1%
3.101263431 1
< 0.1%
3.113052988 1
< 0.1%
ValueCountFrequency (%)
222.0539525 1
< 0.1%
219.506371 1
< 0.1%
218.5617605 1
< 0.1%
213.4622013 1
< 0.1%
204.3747863 1
< 0.1%
201.6308088 1
< 0.1%
201.3693073 1
< 0.1%
194.1690447 1
< 0.1%
193.7847441 1
< 0.1%
188.1042553 1
< 0.1%
Distinct50
Distinct (%)0.1%
Missing709
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean505.45631
Minimum480
Maximum530
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:30:10.627880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum480
5-th percentile483
Q1496
median500
Q3520
95-th percentile529
Maximum530
Range50
Interquartile range (IQR)24

Descriptive statistics

Standard deviation13.615828
Coefficient of variation (CV)0.026937695
Kurtosis-1.1744788
Mean505.45631
Median Absolute Deviation (MAD)10
Skewness0.16181679
Sum26281201
Variance185.39077
MonotonicityIncreasing
2023-07-08T17:30:10.727243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
499 5494
 
10.4%
520 4482
 
8.5%
494 4145
 
7.9%
496 3733
 
7.1%
523 3041
 
5.8%
483 2686
 
5.1%
511 2599
 
4.9%
517 2395
 
4.5%
497 2314
 
4.4%
491 1577
 
3.0%
Other values (40) 19529
37.1%
ValueCountFrequency (%)
480 153
 
0.3%
481 197
 
0.4%
482 733
 
1.4%
483 2686
5.1%
484 101
 
0.2%
485 45
 
0.1%
486 54
 
0.1%
487 88
 
0.2%
488 685
 
1.3%
489 53
 
0.1%
ValueCountFrequency (%)
530 1447
2.7%
529 1161
 
2.2%
528 506
 
1.0%
527 6
 
< 0.1%
526 1
 
< 0.1%
525 413
 
0.8%
524 438
 
0.8%
523 3041
5.8%
522 1110
 
2.1%
521 999
 
1.9%

Interactions

2023-07-08T17:30:06.080364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:52.427056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:53.489657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:54.601026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:55.713894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:56.878560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:57.987798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:59.117657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:00.358451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:01.501179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:02.645632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:03.823656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:05.001197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:06.158445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:52.503518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:53.568123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:54.680034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:55.788913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:56.957953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:58.068542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:59.196744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:00.440858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:01.584487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:02.724254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:03.899032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:05.079554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:06.247640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:52.589129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:53.656235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:54.768540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:55.874512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:57.047404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:58.159940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:59.287226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:00.532198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:01.675294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:02.812187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:03.985145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:05.165101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:06.334352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:52.677594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:53.742982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:54.855135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:55.957653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:57.135469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:58.250048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:59.376028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:00.624035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:01.768542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:02.899981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:04.070350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:05.252042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:06.414847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:52.756286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:53.823801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:54.936247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:56.032713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:57.214508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:58.331994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:59.457950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:00.705681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:01.850502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:02.977091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:04.146618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:05.328671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:06.500921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:52.840697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:53.910266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:55.022414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:56.114651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:57.300770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:58.420120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:59.546983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:00.795022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:01.940381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:03.074681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:04.349067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:05.414146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:06.592608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:52.928252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:54.002896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:55.115668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:56.201619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:57.391161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:58.512736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:59.640783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:00.889961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:02.034444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:03.183285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:04.434850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:05.503118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:06.680854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:53.010696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:54.090900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:55.205917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:56.399435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:57.481383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:58.602246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:59.728948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:00.980211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:02.126300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:03.282839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:04.519603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:05.589505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:06.771180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:53.096895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:54.182188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:55.296973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:56.483370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:57.569540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:58.693745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:59.820984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:01.071069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:02.217941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:03.387709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:04.604481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:05.676009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:06.860950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:53.181941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:54.272308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:55.386301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:56.569153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:57.659716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:58.784816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:59.911679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:01.163280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:02.309148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:03.488745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:04.691578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:05.763361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:06.941205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:53.258678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:54.354120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:55.468123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:56.644848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:57.739608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:58.866376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:59.993364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:01.248019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:02.391945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:03.577966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:04.767793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:05.843951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:07.023449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:53.333622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:54.434859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:55.548293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:56.721273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:57.821478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:58.949157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:00.190660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:01.328879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:02.474117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:03.663797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:04.844356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:05.920956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:07.104700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:53.408783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:54.516459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:55.629668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:56.797232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:57.901833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:29:59.030793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:00.271755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:01.413420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:02.558983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:03.740230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:04.920720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:05.998904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-08T17:30:10.814739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Power (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
Power (kW)1.000-0.015-0.021-0.1280.6660.9930.992-0.1850.8750.5630.9740.794-0.097
Wind direction (°)-0.0151.0000.9270.049-0.046-0.016-0.015-0.102-0.0330.223-0.0290.1350.049
Nacelle position (°)-0.0210.9271.0000.053-0.052-0.022-0.020-0.097-0.0390.216-0.0350.1280.057
blade_angle-0.1280.0490.0531.000-0.500-0.138-0.1390.110-0.290-0.030-0.0980.013-0.077
Rear bearing temperature (°C)0.666-0.046-0.052-0.5001.0000.6660.6610.0750.8350.3320.6460.4560.042
Rotor speed (RPM)0.993-0.016-0.022-0.1380.6661.0000.999-0.1800.8760.5590.9670.789-0.092
Generator RPM (RPM)0.992-0.015-0.020-0.1390.6610.9991.000-0.1930.8770.5600.9670.789-0.098
Nacelle ambient temperature (°C)-0.185-0.102-0.0970.1100.075-0.180-0.1931.000-0.142-0.163-0.165-0.1820.161
Front bearing temperature (°C)0.875-0.033-0.039-0.2900.8350.8760.877-0.1421.0000.4610.8500.665-0.066
Tower Acceleration X (mm/ss)0.5630.2230.216-0.0300.3320.5590.560-0.1630.4611.0000.5110.841-0.089
Wind speed (m/s)0.974-0.029-0.035-0.0980.6460.9670.967-0.1650.8500.5111.0000.773-0.091
Tower Acceleration y (mm/ss)0.7940.1350.1280.0130.4560.7890.789-0.1820.6650.8410.7731.000-0.123
Metal particle count counter-0.0970.0490.057-0.0770.042-0.092-0.0980.161-0.066-0.089-0.091-0.1231.000

Missing values

2023-07-08T17:30:07.231061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-08T17:30:07.429330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-08T17:30:07.658688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
02020-01-01 00:00:00201.244059110.002825104.0011980.00000063.1125018.9036261058.6802985.91250065.377501102.5019814.42883437.440704480.0
12020-01-01 00:10:00155.782218107.011950104.0011980.07349962.1924998.4732231007.6810595.98750063.93249987.7597014.12975533.666105480.0
22020-01-01 00:20:00107.969586109.147373104.0011980.30083261.4650008.269371982.9394595.78500062.312500109.7308503.54832243.752680480.0
32020-01-01 00:30:00100.461781104.325856104.0011980.31816760.9075008.210685976.0351305.46250061.19749979.3544283.91738132.892633480.0
42020-01-01 00:40:0081.381499109.784536104.0011980.49250060.1050018.179880972.0378005.52250059.94250184.6375173.20328226.398953480.0
52020-01-01 00:50:00309.924646128.224023112.2945400.08870361.3166669.6294741143.6795495.88333361.40277861.3786864.88051530.647874480.0
62020-01-01 01:00:00560.374582132.561130129.2450260.00000067.22000012.0163641427.2691125.93500068.48250048.9047516.43878123.442062480.0
72020-01-01 01:10:00356.508199133.780661129.2450260.00000066.11750110.4538381240.3263705.90750068.70750056.5263075.47406927.880304480.0
82020-01-01 01:20:00341.509269127.775100129.2450260.00000065.48750010.2629851219.1547375.91000068.19750045.6523675.68492122.361960480.0
92020-01-01 01:30:00360.227519131.833559129.2450260.00000066.22250110.4534221241.2084645.90000068.76000144.0183045.53530718.884645480.0
# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
526942020-12-31 22:20:00-1.706133298.370465318.02505589.98999815.32500.00.7339341.19018.2075019.3854793.4420875.807321530.0
526952020-12-31 22:30:00-2.985466305.949819318.02505589.98999815.23500.01.8120621.20518.1550015.8694062.9385946.001658530.0
526962020-12-31 22:40:00-1.792560287.599946318.02505589.98999815.24250.01.3259501.13518.0900005.5834023.5169387.763007530.0
526972020-12-31 22:50:00-2.143603291.392719315.00783589.98999815.04500.01.5913891.16017.9000004.9150982.2656199.916087530.0
526982020-12-31 23:00:00-2.975863288.809548310.34210289.98999815.00000.00.7203941.30017.8299994.6800362.2461386.433806530.0
526992020-12-31 23:10:00-1.788826290.462220310.34210289.98999814.96000.00.6910991.30017.6450004.1898452.0891443.968115530.0
527002020-12-31 23:20:00-2.505316290.250317310.34210289.98999814.75000.00.6712391.30017.5225004.0447942.0366065.765754530.0
527012020-12-31 23:30:00-2.322326291.619628310.34210289.98999814.70000.00.7227421.30017.4875004.1244202.2863384.823318530.0
527022020-12-31 23:40:00-2.401283289.874793310.34210289.98999814.70750.00.6783711.37017.4050005.4865713.0133317.326086530.0
527032020-12-31 23:50:00-1.913665294.063925310.34210289.98999814.45000.00.7180021.35017.2900005.3474432.3484755.883299530.0